# CD: copy pasted from https://raw.githubusercontent.com/iShohei220/adopt/refs/heads/main/adopt.py
# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
from typing import cast, Callable, List, Optional, Tuple, Union
import torch
from torch import Tensor
from torch.optim.optimizer import (
_capturable_doc, # noqa: F401
_default_to_fused_or_foreach,
_device_dtype_check_for_fused,
_differentiable_doc, # noqa: F401
_disable_dynamo_if_unsupported,
_foreach_doc, # noqa: F401
_fused_doc, # noqa: F401
_get_capturable_supported_devices,
_get_scalar_dtype,
_get_value, # noqa: F401
_maximize_doc, # noqa: F401
_stack_if_compiling, # noqa: F401
_use_grad_for_differentiable,
_view_as_real,
DeviceDict, # noqa: F401
Optimizer,
ParamsT,
)
__all__ = ["ADOPT", "adopt"]
[docs]
class ADOPT(Optimizer):
def __init__(
self,
params: ParamsT,
lr: Union[float, Tensor] = 1e-3,
betas: Tuple[float, float] = (0.9, 0.9999),
eps: float = 1e-6,
clip_lambda: Optional[Callable[[int], float]] = lambda step: step**0.25,
weight_decay: float = 0.0,
decouple: bool = False,
*,
foreach: Optional[bool] = None,
maximize: bool = False,
capturable: bool = False,
differentiable: bool = False,
fused: Optional[bool] = None,
):
if isinstance(lr, Tensor):
if foreach and not capturable:
raise ValueError(
"lr as a Tensor is not supported for capturable=False and foreach=True"
)
if lr.numel() != 1:
raise ValueError("Tensor lr must be 1-element")
if not 0.0 <= lr:
raise ValueError(f"Invalid learning rate: {lr}")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps}")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
self.clip_lambda = clip_lambda
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
decouple=decouple,
maximize=maximize,
foreach=foreach,
capturable=capturable,
differentiable=differentiable,
fused=fused,
)
super().__init__(params, defaults)
if fused:
# TODO: support fused
raise RuntimeError("`fused` is not currently supported")
if differentiable:
raise RuntimeError("`fused` does not support `differentiable`")
self._step_supports_amp_scaling = True
# TODO(crcrpar): [low prec params & their higher prec copy]
# Support AMP with FP16/BF16 model params which would need
# higher prec copy of params to do update math in higher prec to
# alleviate the loss of information.
if foreach:
raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
[docs]
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("maximize", False)
group.setdefault("foreach", None)
group.setdefault("capturable", False)
group.setdefault("differentiable", False)
fused = group.setdefault("fused", None)
for p in group["params"]:
p_state = self.state.get(p, [])
if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
step_val = float(p_state["step"])
p_state["step"] = (
torch.tensor(
step_val,
dtype=_get_scalar_dtype(is_fused=fused),
device=p.device,
)
if group["capturable"] or group["fused"]
else torch.tensor(step_val, dtype=_get_scalar_dtype())
)
def _init_group(
self,
group,
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
state_steps,
):
has_complex = False
for p in group["params"]:
if p.grad is not None:
has_complex |= torch.is_complex(p)
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError(
"ADOPT does not support sparse gradients"
)
grads.append(p.grad)
state = self.state[p]
# Lazy state initialization
if len(state) == 0:
if group["fused"]:
_device_dtype_check_for_fused(p)
# note(crcrpar): [special device hosting for step]
# Deliberately host `step` on CPU if both capturable and fused are off.
# This is because kernel launches are costly on CUDA and XLA.
state["step"] = (
torch.zeros(
(),
dtype=_get_scalar_dtype(is_fused=group["fused"]),
device=p.device,
)
if group["capturable"] or group["fused"]
else torch.tensor(0.0, dtype=_get_scalar_dtype())
)
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
exp_avgs.append(state["exp_avg"])
exp_avg_sqs.append(state["exp_avg_sq"])
if group["differentiable"] and state["step"].requires_grad:
raise RuntimeError(
"`requires_grad` is not supported for `step` in differentiable mode"
)
# Foreach without capturable does not support a tensor lr
if (
group["foreach"]
and torch.is_tensor(group["lr"])
and not group["capturable"]
):
raise RuntimeError(
"lr as a Tensor is not supported for capturable=False and foreach=True"
)
state_steps.append(state["step"])
return has_complex
[docs]
@_use_grad_for_differentiable
def step(self, closure=None):
"""Perform a single optimization step.
Args:
closure (Callable, optional): A closure that reevaluates the model
and returns the loss.
"""
self._cuda_graph_capture_health_check()
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad: List[Tensor] = []
grads: List[Tensor] = []
exp_avgs: List[Tensor] = []
exp_avg_sqs: List[Tensor] = []
state_steps: List[Tensor] = []
beta1, beta2 = group["betas"]
has_complex = self._init_group(
group,
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
state_steps,
)
adopt(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
state_steps,
has_complex=has_complex,
beta1=beta1,
beta2=beta2,
lr=group["lr"],
clip_lambda=self.clip_lambda,
weight_decay=group["weight_decay"],
decouple=group["decouple"],
eps=group["eps"],
maximize=group["maximize"],
foreach=group["foreach"],
capturable=group["capturable"],
differentiable=group["differentiable"],
fused=group["fused"],
grad_scale=getattr(self, "grad_scale", None),
found_inf=getattr(self, "found_inf", None),
)
return loss
def _single_tensor_adopt(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
state_steps: List[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
has_complex: bool,
beta1: float,
beta2: float,
lr: Union[float, Tensor],
clip_lambda: Optional[Callable[[int], float]],
weight_decay: float,
decouple: bool,
eps: float,
maximize: bool,
capturable: bool,
differentiable: bool,
):
assert grad_scale is None and found_inf is None
if torch.jit.is_scripting():
# this assert is due to JIT being dumb and not realizing that the ops below
# have overloads to handle both float and Tensor lrs, so we just assert it's
# a float since most people using JIT are using floats
assert isinstance(lr, float)
for i, param in enumerate(params):
grad = grads[i] if not maximize else -grads[i]
exp_avg = exp_avgs[i]
exp_avg_sq = exp_avg_sqs[i]
step_t = state_steps[i]
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices()
assert (
param.device.type == step_t.device.type
and param.device.type in capturable_supported_devices
), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
step = step_t if capturable or differentiable else _get_value(step_t)
if weight_decay != 0 and not decouple:
grad = grad.add(param, alpha=weight_decay)
if torch.is_complex(param):
grad = torch.view_as_real(grad)
if exp_avg is not None:
exp_avg = torch.view_as_real(exp_avg)
if exp_avg_sq is not None:
exp_avg_sq = torch.view_as_real(exp_avg_sq)
param = torch.view_as_real(param)
if step == 0:
exp_avg_sq.addcmul_(grad, grad.conj())
# update step
step_t += 1
continue
if weight_decay != 0 and decouple:
param.add_(param, alpha=-lr*weight_decay)
denom = torch.clamp(exp_avg_sq.sqrt(), eps)
normed_grad = grad.div(denom)
if clip_lambda is not None:
clip = clip_lambda(step)
normed_grad.clamp_(-clip, clip)
exp_avg.lerp_(normed_grad, 1 - beta1)
param.add_(exp_avg, alpha=-lr)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
# update step
step_t += 1
def _multi_tensor_adopt(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
state_steps: List[Tensor],
grad_scale: Optional[Tensor],
found_inf: Optional[Tensor],
*,
has_complex: bool,
beta1: float,
beta2: float,
lr: Union[float, Tensor],
clip_lambda: Optional[Callable[[int], float]],
weight_decay: float,
decouple: bool,
eps: float,
maximize: bool,
capturable: bool,
differentiable: bool,
):
if len(params) == 0:
return
if isinstance(lr, Tensor) and not capturable:
raise RuntimeError(
"lr as a Tensor is not supported for capturable=False and foreach=True"
)
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices(
supports_xla=False
)
assert all(
p.device.type == step.device.type
and p.device.type in capturable_supported_devices
for p, step in zip(params, state_steps)
), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
assert grad_scale is None and found_inf is None
assert not differentiable, "_foreach ops don't support autograd"
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, exp_avgs, exp_avg_sqs, state_steps] # type: ignore[list-item]
)
for (
device_params_,
device_grads_,
device_exp_avgs_,
device_exp_avg_sqs_,
device_state_steps_,
), _ in grouped_tensors.values():
device_params = cast(List[Tensor], device_params_)
device_grads = cast(List[Tensor], device_grads_)
device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
device_state_steps = cast(List[Tensor], device_state_steps_)
# Handle complex parameters
if has_complex:
_view_as_real(
device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
)
if maximize:
device_grads = torch._foreach_neg(device_grads) # type: ignore[assignment]
if weight_decay != 0 and not decouple:
# Re-use the intermediate memory (device_grads) already allocated for maximize
if maximize:
torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
else:
device_grads = torch._foreach_add( # type: ignore[assignment]
device_grads, device_params, alpha=weight_decay
)
if device_state_steps[0] == 0:
torch._foreach_addcmul_(device_exp_avg_sqs, device_grads, device_grads)
# Update steps
# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
# wrapped it once now. The alpha is required to assure we go to the right overload.
if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
torch._foreach_add_(
device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
)
else:
torch._foreach_add_(device_state_steps, 1)
continue
if weight_decay != 0 and decouple:
torch._foreach_add_(device_params, device_params, alpha=-lr*weight_decay)
exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
torch._foreach_maximum_(exp_avg_sq_sqrt, eps)
normed_grad = torch._foreach_div(device_grads, exp_avg_sq_sqrt)
if clip_lambda is not None:
clip = clip_lambda(device_state_steps[0])
torch._foreach_maximum_(normed_grad, -clip)
torch._foreach_minimum_(normed_grad, clip)
torch._foreach_lerp_(device_exp_avgs, normed_grad, 1 - beta1)
torch._foreach_add_(device_params, device_exp_avgs, alpha=-lr)
torch._foreach_mul_(device_exp_avg_sqs, beta2)
torch._foreach_addcmul_(
device_exp_avg_sqs, device_grads, device_grads, value=1 - beta2
)
# Update steps
# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
# wrapped it once now. The alpha is required to assure we go to the right overload.
if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
torch._foreach_add_(
device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
)
else:
torch._foreach_add_(device_state_steps, 1)
[docs]
@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adopt)
def adopt(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
state_steps: List[Tensor],
# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
foreach: Optional[bool] = None,
capturable: bool = False,
differentiable: bool = False,
fused: Optional[bool] = None,
grad_scale: Optional[Tensor] = None,
found_inf: Optional[Tensor] = None,
has_complex: bool = False,
*,
beta1: float,
beta2: float,
lr: Union[float, Tensor],
clip_lambda: Optional[Callable[[int], float]],
weight_decay: float,
decouple: bool,
eps: float,
maximize: bool,
):
r"""Functional API that performs ADOPT algorithm computation.
"""
# Respect when the user inputs False/True for foreach or fused. We only want to change
# the default when neither have been user-specified. Note that we default to foreach
# and pass False to use_fused. This is not a mistake--we want to give the fused impl
# bake-in time before making it the default, even if it is typically faster.
if fused is None and foreach is None:
_, foreach = _default_to_fused_or_foreach(
params, differentiable, use_fused=False
)
# Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
if foreach and isinstance(lr, Tensor) and not capturable:
foreach = False
if fused is None:
fused = False
if foreach is None:
foreach = False
# this check is slow during compilation, so we skip it
# if it's strictly needed we can add this check back in dynamo
if not torch._utils.is_compiling() and not all(
isinstance(t, torch.Tensor) for t in state_steps
):
raise RuntimeError(
"API has changed, `state_steps` argument must contain a list of singleton tensors"
)
if foreach and torch.jit.is_scripting():
raise RuntimeError("torch.jit.script not supported with foreach optimizers")
if fused and torch.jit.is_scripting():
raise RuntimeError("torch.jit.script not supported with fused optimizers")
if fused and not torch.jit.is_scripting():
func = _fused_adopt # noqa: F821
elif foreach and not torch.jit.is_scripting():
func = _multi_tensor_adopt
else:
func = _single_tensor_adopt
func(
params,
grads,
exp_avgs,
exp_avg_sqs,
state_steps,
has_complex=has_complex,
beta1=beta1,
beta2=beta2,
lr=lr,
clip_lambda=clip_lambda,
weight_decay=weight_decay,
decouple=decouple,
eps=eps,
maximize=maximize,
capturable=capturable,
differentiable=differentiable,
grad_scale=grad_scale,
found_inf=found_inf,
)